Robust mean-squared error estimation in the presence of model uncertainties
IEEE Transactions on Signal Processing
A competitive minimax approach to robust estimation of random parameters
IEEE Transactions on Signal Processing
Universal Switching Linear Least Squares Prediction
IEEE Transactions on Signal Processing
MIMO decision feedback equalization from an H∞ perspective
IEEE Transactions on Signal Processing
Robust estimation in flat fading channels under bounded channel uncertainties
Digital Signal Processing
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We investigate a linear estimation problem under model uncertainties using a competitive algorithm framework under mean square error (MSE) criteria. Here, the performance of a linear estimator is defined relative to the performance of the linear minimum MSE estimator tuned to the underlying unknown system model. We then find the linear estimator that minimizes this relative performance measure, i.e., the regret, for the worst possible system model. Two definitions of regret are given: first as a difference of MSEs and second as a ratio of MSEs. We demonstrate that finding the linear estimators that minimize these regret definitions can be cast as a Semidefinite Programming (SDP) problem and provide numerical examples.